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Implementation of model predictive control in tracking dynamic optimal profiles of semi batch autocatalytic esterification reactor
Author(s) -
Rohman Fakhrony S.,
Muhammad Dinie,
Aziz Norashid
Publication year - 2020
Publication title -
asia‐pacific journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.348
H-Index - 35
eISSN - 1932-2143
pISSN - 1932-2135
DOI - 10.1002/apj.2418
Subject(s) - model predictive control , dynamic programming , optimal control , nonlinear system , control theory (sociology) , mathematical optimization , computer science , nonlinear programming , trajectory , nonlinear model , solver , autocatalysis , batch processing , mathematics , control (management) , chemistry , programming language , biochemistry , physics , artificial intelligence , astronomy , catalysis , quantum mechanics
Optimization of ester production that yields from sec‐butyl propionate in a batch operation mode need to be solved by dynamic–nonlinear programming‐based optimization because the dynamics of this autocatalytic esterification process can be described using the detailed first principle model. In order to maximize profit, the control vector parameterisation technique combined with a hybrid strategy, that is, a deterministic–stochastic nonlinear programming solver, is implemented by generating optimal temperature and feed flowrate trajectories. The final time and profit achieved is 60.0 min and RM 12.840 min −1 , respectively. Model predictive control is then applied to track the optimal temperature trajectory obtained from the maximize profit study. The result reveals that the model predictive control is able to track the optimal temperature trajectory very well and consequently able to maintain the on‐spec product profit.